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Towards personalized medical document classificationby leveraging UMLS Ssemantic network

Petrakis Evripidis, Lakiotaki Kleanthi, Chliaoutakis Angelos, Koutsos Serafeim

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URIhttp://purl.tuc.gr/dl/dias/7B0AE011-CBD1-4018-9CEA-94889C6C7F66-
Identifierhttps://doi.org/10.1007/978-3-642-37899-7_8-
Languageen-
TitleTowards personalized medical document classification by leveraging UMLS Ssemantic networken
CreatorPetrakis Evripidisen
CreatorΠετρακης Ευριπιδηςel
CreatorLakiotaki Kleanthien
CreatorΛακιωτακη Κλεανθηel
CreatorChliaoutakis Angelosen
CreatorΧλιαουτακης Αγγελοςel
CreatorKoutsos Serafeimen
CreatorΚουτσος Σεραφειμel
PublisherSpringer Verlagen
Content SummaryThe overwhelmed amount of medical information available in the research literature, makes the use of automated information classification methods essential for both medical experts and novice users. This paper presents a method for classifying medical documents into documents for medical professionals (experts) and non-professionals (consumers), by representing them as term vectors and applying Multiple Criteria Decision Analysis (MCDA) tools to leverage this information. The results show that when medical documents are represented by terms extracted from AMTEx, a medical document indexing method, specifically designed for the automatic indexing of documents in large medical collections, such as MEDLINE, better classification performance is achieved, compared to MetaMap Transfer, the automatic mapping of biomedical documents to UMLS term concepts developed by U.S. National Library of Medicine, or the MeSH method, under which documents are indexed by human experts.en
Type of ItemΠερίληψη Δημοσίευσης σε Συνέδριοel
Type of ItemConference Paper Abstracten
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-31-
Date of Publication2013-
Bibliographic CitationKleanthi Lakiotaki, Angelos Hliaoutakis, Serafim Koutsos, Euripides G.M. Petrakis, "Towards Personalized Medical Document Classification by Leveraging UMLS Semantic Network", in 2nd International Conference on Health Information Science (HIS2013), 2013, pp.93-104. doi:10.1007/978-3-642-37899-7_8en

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